Asset Administration Shell (AAS) serves as the digital representation of industrial assets, facilitating the creation and management of Industry 4.0 digital twins. In the context of integrating 5G into the industrial domain, AAS addresses the requirements of industrial applications and operational technology (OT) processes by providing an abstraction layer that enables factory operators to manage networks according to their needs. This seamless horizontal integration of 5G networks into OT processes lays the foundation for a holistic, end-to-end automation framework. Moreover, AAS offers valuable insights from diverse systems that enhance both network and asset management performance. In this paper, we detail the design, development, and implementation of 5G AAS, which encompasses both the 5G network and 5G user equipment components. We also demonstrate the practical application of our approach through the integration of the 5G-Industry Campus Europe and a corresponding use case.

The growing complexity of networks and the variety of future scenarios with diverse and often stringent performance requirements call for a higher level of automation. Intent-based management emerges as a solution to attain high level of automation, enabling human operators to solely communicate with the network through high-level intents. The intents consist of the targets in the form of expectations (i.e., latency expectation) from a service and based on the expectations the required network configurations should be done accordingly. It is almost inevitable that when a network action is taken to fulfill one intent, it can cause negative impacts on the performance of another intent, which results in a conflict. In this paper, we address the challenge of conflict resolution in intent-based networking and propose an online learning approach based on the hierarchical multi-armed bandit framework for autonomous network management. The hierarchical structure enables efficient exploration and exploitation of network configurations while adapting to dynamic network conditions. Our proposed hierarchical multi-armed bandit conflict resolution (MABCR) approach optimizes resource allocation within a partially known system with limited bandwidth. In comparison to other approaches, we show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.

Efficient indoor positioning of the industrial devices is a key pillar of the digitalized factories. In 5G/6G era, the integration of positioning, the network communication and the environmental information is important to enhance the accuracy and performance for various use cases. In this direction, there is a need of a unified framework that is able to aggregate knowledge about device positioning from multiple systems and provide value added services for the vertical industries to realize their use cases. In this paper, we propose a positioning service function that combines the relevant information gained from different sources for providing enhanced positioning services with predictions. In addition, this paper presents a methodology based on Asset Administration Shell (AAS) to implement the proposed functionalities. The proposed approach outlines future direction on enhancing positioning services in smart factories to improve network and service management. We further illustrate our suggested way forward through a smart manufacturing use case.

An intent is the formal specification of all expectations including requirements, goals, and constraints given to a technical system. Intent-based management is considered as one of the key enablers of autonomous network management and service assurance mechanisms. However, the use of intents in industrial networks to achieve end-to-end automation has not been addressed yet. The intent handling operations in an industrial setting require particular vocabulary and semantics to provide specialized support for industrial requirements. Therefore, this paper proposes to extend the intent common model introduced by the TM Forum and defines an intent extension model to simplify the management of the industrial networks supporting enterprises. Besides, the vision towards achieving an integrated intent-based automation for industries is introduced.

Intent-based network operation is an essential paradigm to create autonomous networks as it eliminates the complexity of manual configurations and replaces it with an abstracted, automated method. To enable intent-based networks that can handle and resolve conflicts under shared and limited network resources, prediction of the impact of proposed configuration and topology changes is crucial. In this respect, Graph Neural Networks (GNN)s are gaining attention in the networking domain thanks to their ability to process data based on close to real modeling of networks as a graph. In this paper, we develop a GNN framework of intent-based networks that takes the network information as inputs and predicts the specific key performance indicators (KPIs) of intents. We propose to make use of GNNs for accurate prediction of the impacts of proposed actions on all of the active KPIs of an intent-based network. Experiments with different configurations are conducted and the empirical evaluations demonstrate that a GNN based prediction agent outperforms a baseline neural-network based agent in both prediction performance and ability to generalize to previously unseen configurations.

The factory operators lack 3GPP network knowledge and there is a requirement for industrial 5G networks to enable zero-touch automation which will translate factory business expectations to technical expectations. Integrating the intent-based operations is a key approach to implement intelligent network management system which can automatically verify, deploy, configure and optimize itself to meet the targets and goals. However, there is still a need for human intervention (e.g., factory operators) to place the intents. In this paper, we propose a solution based on Asset Administration Shell (AAS) to implement digital twins of the 5G network and 5G-capable factory devices. The proposed solution approach implements necessary submodels in AAS instances, so that the factory devices can place their own intents with minimized manual intervention. This framework is implemented and demonstrated with an automation system in conjunction with a 5G emulation environment.

The ever-growing complexity in networks and the various future use cases with diverse, and often stringent performance requirements call for a higher level of automation. A tool to achieve this higher level of automation is intent-based management, where the human operator only communicates via high-level intents with the network. Intents are declarative in nature, they specify the desired state but don’t specify how to achieve it. The system will assure that the intents are fulfilled, by monitoring the behavior of the network, and adjusting its configurations if needed. In future networks that will serve multiple tenants and use cases at the same time, many intents will co-exist. As the resources are limited, conflicts may arise between intents. Therefore, a solution is needed for conflict detection and resolution. In this paper, we present a system that is capable of handling multiple intents and detecting and resolving conflicts at run-time. To show the feasibility, we implement our approach in an end-to-end prototype.